code: def testCriticLoss(self):
      agent = ddpg_agent.DdpgAgent(
          self._time_step_spec,
          self._action_spec,
          actor_network=self._unbounded_actor_net,
          critic_network=self._critic_net,
          actor_optimizer=None,
          critic_optimizer=None,
      )
  
      observations = [tf.constant([[1, 2], [3, 4]], dtype=tf.float32)]
      time_steps = ts.restart(observations, batch_size=2)
  
      actions = [tf.constant([[5], [6]], dtype=tf.float32)]
  
      rewards = tf.constant([10, 20], dtype=tf.float32)
      discounts = tf.constant([0.9, 0.9], dtype=tf.float32)
      next_observations = [
          tf.constant([[5, 6], [7, 8]], dtype=tf.float32)
      ]
      next_time_steps = ts.transition(
          next_observations, rewards, discounts
      )
  
      expected_loss = 59.6
      loss = agent.critic_loss(time_steps, actions, next_time_steps)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      loss_ = self.evaluate(loss)
      self.assertAllClose(loss_, expected_loss)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.agents.ddpg import ddpg_agent
  from tf_agents.networks import network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 3 for calling the DdpgAgentTest obj's underlying member method code with example usage: obj.testCriticLoss() without return types